127 lines
4.6 KiB
Python
127 lines
4.6 KiB
Python
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import pm4py
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from pm4py.visualization.petri_net import visualizer as pn_visualizer
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parameters = {pn_visualizer.Variants.FREQUENCY.value.Parameters.FORMAT: "png"}
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###### Load data and create event logs ######
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dat = pd.read_csv("results/haum/event_logfiles_2024-01-18_09-58-52.csv", sep = ";")
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dat = dat[dat["date.start"] < "2020-03-13"]
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dat = dat[dat["path"] != 106098] # exclude broken trace
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# --> only pre corona (before artworks were updated)
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event_log = pm4py.format_dataframe(dat, case_id='case', activity_key='event',
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timestamp_key='date.start')
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event_log.event.value_counts()
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event_log.event.value_counts(normalize=True)
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dfg, start_activities, end_activities = pm4py.discover_dfg(event_log)
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pm4py.view_dfg(dfg, start_activities, end_activities)
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#filtered_log = pm4py.filter_event_attribute_values(event_log, 'item', [80])
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net, im, fm = pm4py.discover_petri_net_inductive(event_log)
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pm4py.vis.view_petri_net(net, im, fm)
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pm4py.vis.view_petri_net(net, im, fm)
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gviz = pn_visualizer.apply(net, im, fm, parameters=parameters,
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variant=pn_visualizer.Variants.FREQUENCY,
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log=event_log)
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pn_visualizer.view(gviz)
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bpmn = pm4py.convert.convert_to_bpmn(net, im, fm)
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pm4py.vis.view_bpmn(bpmn)
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net2, im2, fm2 = pm4py.discover_petri_net_inductive(event_log, noise_threshold=0.1)
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pm4py.vis.view_petri_net(net2, im2, fm2)
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def eval_pm(data, net, initial_marking, final_marking):
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"""Caculate fitness, precision, generalizability, and simplicity for petri net"""
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fitness = pm4py.fitness_token_based_replay(data, net, initial_marking, final_marking)
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precisison = pm4py.precision_token_based_replay(data, net, initial_marking, final_marking)
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#generalizability = pm4py.algo.evaluation.generalization.algorithm.apply(data, net,
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# initial_marking, final_marking)
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simplicity = pm4py.algo.evaluation.simplicity.algorithm.apply(net)
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#return [fitness['average_trace_fitness'], precisison, generalizability, simplicity]
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return [fitness['average_trace_fitness'], precisison, simplicity]
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eval = eval_pm(event_log, net, im, fm)
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eval2 = eval_pm(event_log, net2, im2, fm2)
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len(net.places)
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len(net.transitions)
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len(net.arcs)
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# Number of cases
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len(event_log.case.unique())
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# Number of variants
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variants = pm4py.get_variants(event_log)
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len(variants)
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sorted_variants = dict(sorted(variants.items(), key=lambda item: item[1], reverse = True))
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{k: sorted_variants[k] for k in list(sorted_variants)[:20]}
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filtered_log = event_log[event_log["event"] != "move"]
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variants_no_move = pm4py.get_variants(filtered_log)
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len(variants_no_move)
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sorted_variants_no_move = dict(sorted(variants_no_move.items(), key=lambda item: item[1], reverse = True))
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{k: sorted_variants_no_move[k] for k in list(sorted_variants_no_move)[:20]}
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###### Navigation behavior for case ######
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log_case = pm4py.format_dataframe(dat, case_id = "case", activity_key = "item",
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timestamp_key = "date.start")
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log_case = log_case.merge(tmp, on = "item", how = "left")
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#filtered_log = pm4py.filter_event_attribute_values(log_case, "kcluster", [3])
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filtered_log = log_case[log_case.hcluster == 1]
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net, im, fm = pm4py.discover_dfg(filtered_log)
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pm4py.vis.view_dfg(net, im, fm)
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net, im, fm = pm4py.discover_petri_net_inductive(filtered_log)
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pm4py.vis.view_petri_net(net, im, fm)
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tree = pm4py.discovery.discover_process_tree_inductive(filtered_log)
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pm4py.vis.view_process_tree(tree)
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datcase = dat[~dat.duplicated(["case", "path", "item"])]
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datcase = datcase[["case", "path", "event", "item", "date.start"]]
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datcase = datcase.reset_index().drop("index", axis = 1)
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#datcase = pd.concat([datcase, pd.get_dummies(datcase["item"], dtype = "int")], axis = 1)
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datcase["duration"] = dat.groupby("path")["duration"].mean().tolist()
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datcase["distance"] = dat.groupby("path")["distance"].mean().tolist()
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datcase["scaleSize"] = dat.groupby("path")["scaleSize"].mean().tolist()
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datcase["rotationDegree"] = dat.groupby("path")["rotationDegree"].mean().tolist()
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datcase["item"] = [str(item).zfill(3) for item in datcase.item]
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datcase = datcase.merge(xy[["item", "hcluster"]], on = "item", how = "left")
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log_case = pm4py.format_dataframe(dat, case_id = "case", activity_key = "item",
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timestamp_key = "date.start")
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net, im, fm = pm4py.discover_dfg(log_case)
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pm4py.vis.view_dfg(net, im, fm)
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# don't know if this will eventually finish?
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net, im, fm = pm4py.discover_dfg(log_case[log_case.hcluster == 1])
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pm4py.vis.view_dfg(net, im, fm)
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